Target the neglected VOCs emission from iron and steel industry in China for air quality improvement

● Haze formation in China is highly correlated with iron and steel industry. ● VOCs generated in sinter process were neglected under current emission standard. ● Co-elimination removal of sinter flue gas complex pollutants are timely needed. Recent years have witnessed significant improvement in China’s air quality. Strict environmental protection measures have led to significant decreases in sulfur dioxide (SO₂), nitrogen oxides (NO_x), and particulate matter (PM) emissions since 2013. But there is no denying that the air quality in 135 cities is inferior to reaching the Ambient Air Quality Standards (GB 3095–2012) in 2020. In terms of temporal, geographic, and historical aspects, we have analyzed the potential connections between China’s air quality and the iron and steel industry. The non-target volatile organic compounds (VOCs) emissions from iron and steel industry, especially from the iron ore sinter process, may be an underappreciated index imposing a negative effect on the surrounding areas of China. Therefore, we appeal the authorities to pay more attention on VOCs emission from the iron and steel industry and establish new environmental standards. And different iron steel flue gas pollutants will be eliminated concurrently with the promotion and application of new technology.     

Response of organic aerosol characteristics to emission reduction in Yangtze River Delta region

● The emission reduction causes significant change in organic aerosol composition. ● The atmospheric oxidizing capacity improved during emission reduction. ● The mixed oxygenated organic aerosol contributed higher during emission reduction. Organic aerosol (OA) is a major component of atmospheric particulate matter (PM) with complex composition and formation processes influenced by various factors. Emission reduction can alter both precursors and oxidants which further affects secondary OA formation. Here we provide an observational analysis of secondary OA (SOA) variation properties in Yangtze River Delta (YRD) of eastern China in response to large scale of emission reduction during Chinese New Year (CNY) holidays from 2015 to 2020, and the COVID-19 pandemic period from January to March, 2020. We found a 17% increase of SOA proportion during the COVID lockdown. The relative enrichment of SOA is also found during multi-year CNY holidays with dramatic reduction of anthropogenic emissions. Two types of oxygenated OA (OOA) influenced by mixed emissions and SOA formation were found to be the dominant components during the lockdown in YRD region. Our results highlight that these emission-reduction-induced changes in organic aerosol need to be considered in the future to optimize air pollution control measures.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

Twenty years of achievements in China’s implementation of the Stockholm Convention

● China’s implementation of the SC was systematically studied. ● Implementation process of the SC can be roughly divided into three stages. ● DDT and HCH concentrations in the air have been steadily decreasing. ● China has safely disposed of 6352.1 tons of pesticide POPs. Persistent organic pollutants (POPs) are extremely harmful to the environment and human health; the Stockholm Convention on Persistent Organic Pollutants was therefore adopted by the international community in 2001 to eliminate or reduce the production, use, and emissions of POPs. China is the largest developing country that has signed the Stockholm Convention, and thus plays an important role in its implementation. This paper systematically studies the practice and achievements of China since it signed the Stockholm Convention 20 years ago. China has established an implementation guarantee system including institutions, implementation mechanisms, policies, law enforcement, and scientific and technological support. During the 20 years since the implementation of the Stockholm Convention, dichlorodiphenyltrichloroethane (DDT) and hexachlorocyclohexane (HCH) concentrations in the air have been steadily decreasing, and Perfluorooctane sulfonic acid/Perfluorooctane sulfonyl fluoride (PFOS/PFOSF) concentrations in water bodies have decreased. In the past 20 years, China has safely disposed of 6352.1 tons of pesticide persistent organic pollutants and 36998 sets of electrical equipment containing polychlorinated biphenyls (PCBs), with a disposal rate of 100%. In the future, China will further strengthen the construction of persistent organic pollutant monitoring networks, scientific research, publicity, education, and international cooperation to improve environmental quality, providing a reference for other countries to implement the Stockholm Convention.     

Identify the contribution of vehicle non-exhaust emissions: a single particle aerosol mass spectrometer test case at typical road environment

● A single particle observation was conducted in a high traffic flow road environment. ● Major particle types were vehicle exhausts, coal burning, and biomass burning. ● Contribution of non-exhaust emissions was calculated via PMF. ● Proportion of non-exhaust emissions can reach 10.1 % at road environment. A single particle aerosol mass spectrometer (SPAMS) was used to accurately quantify the contribution of vehicle non-exhaust emissions to particulate matter at typical road environment. The PM_2.5, black carbon, meteorological parameters and traffic flow were recorded during the test period. The daily trend for traffic flow and speed on TEDA Street showed obvious “M” and “W” characteristics. 6.3 million particles were captured via the SPAMS, including 1.3 million particles with positive and negative spectral map information. Heavy Metal, High molecular Organic Carbon, Organic Carbon, Mixed Carbon, Elemental Carbon, Rich Potassium, Levo-rotation Glucose, Rich Na, SiO₃ and other categories were analyzed. The particle number concentration measured by SPAMS showed a good linear correlation with the mass concentrations of PM_2.5 and BC, which indicates that the particulate matter captured by the SPAMS reflects the pollution level of fine particulate matter. EC, ECOC, OC, HM and crustal dust components were found to show high values from 7:00–9:00 AM, showing that these chemical components are directly or indirectly related to vehicle emissions. Based on the PMF model, 7 major factors are resolved. The relative contributions of each factor were determined: vehicle exhaust emission (44.8 %), coal-fired source (14.5 %), biomass combustion (12.2 %), crustal dust (9.4 %), ship emission (9.0 %), tires wear (6.6 %) and brake pads wear (3.5 %). The results show that the contribution of vehicle non-exhaust to particulate matter at roadside environment is approximately 10.1 %. Vehicle non-exhaust emissions are the focus of future research in the vehicle pollutant emission control field.     

Discovery of emerging organic pollutants in the atmosphere through an omics approach

● We review the framework of discovering emerging pollutants through an omics approach. ● High-resolution MS can digitalize atmospheric samples to full-component data. ● Chemical features and databases can help to translate untargeted data to compounds. ● Biological effect-directed untargeted analyses consider both existence and toxicity. Ambient air pollution, containing numerous known and hitherto unknown compounds, is a major risk factor for public health. The discovery of harmful components is the prerequisite for pollution control; however, this raises a great challenge on recognizing previously unknown species. Here we systematically review the analytical techniques on air pollution in the framework of an omics approach, with a brief introduction on sample preparation and analysis, and in more detail, compounds prioritization and identification. Through high-resolution mass spectrometry (HRMS, typically coupled with chromatography), the complicated environmental matrix can be digitalized into “full-component” data. A key step to discover emerging compounds is the prioritization of compounds from massive data. Chemical fingerprints, suspect lists and biological effects are the most vital untargeted strategies for comprehensively screening critical and hazardous substances. Afterward, compressed data of compounds can be identified at various confidence levels according to exact mass and the derived molecular formula, MS libraries, and authentic standards. Such an omics approach on full-component data provides a paradigm for discovering emerging air pollutants; nonetheless, new technological advancements of instruments and databases are warranted for further tracking the environmental behaviors, hence to evaluate the health risk of key pollutants.     

Bacteria inactivation by sulfate radical: progress and non-negligible disinfection by-products

● Status of inactivation of pathogenic microorganisms by SO₄^•− is reviewed. ● Mechanism of SO₄^•− disinfection is outlined. ● Possible generation of DBPs during disinfection using SO₄^•− is discussed. ● Possible problems and challenges of using SO₄^•− for disinfection are presented. Sulfate radicals have been increasingly used for the pathogen inactivation due to their strong redox ability and high selectivity for electron-rich species in the last decade. The application of sulfate radicals in water disinfection has become a very promising technology. However, there is currently a lack of reviews of sulfate radicals inactivated pathogenic microorganisms. At the same time, less attention has been paid to disinfection by-products produced by the use of sulfate radicals to inactivate microorganisms. This paper begins with a brief overview of sulfate radicals’ properties. Then, the progress in water disinfection by sulfate radicals is summarized. The mechanism and inactivation kinetics of inactivating microorganisms are briefly described. After that, the disinfection by-products produced by reactions of sulfate radicals with chlorine, bromine, iodide ions and organic halogens in water are also discussed. In response to these possible challenges, this article concludes with some specific solutions and future research directions.     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.     

Catalytic reduction of water pollutants: knowledge gaps,lessons learned,and new opportunities

● Advances, challenges, and opportunities for catalytic water pollutant reduction. ● Cases of Pd-based catalysts for nitrate, chlorate, and perchlorate reduction. ● New functionalities developed by screening and design of catalytic metal sites. ● Facile catalyst preparation approaches for convenient catalyst optimization. ● Rational design and non-decorative effort are essential for future work. In this paper, we discuss the previous advances, current challenges, and future opportunities for the research of catalytic reduction of water pollutants. We present five case studies on the development of palladium-based catalysts for nitrate, chlorate, and perchlorate reduction with hydrogen gas under ambient conditions. We emphasize the realization of new functionalities through the screening and design of catalytic metal sites, including (i) platinum group metal (PGM) nanoparticles, (ii) the secondary metals for improving the reaction rate and product selectivity of nitrate reduction, (iii) oxygen-atom-transfer metal oxides for chlorate and perchlorate reduction, and (iv) ligand-enhanced coordination complexes for substantial activity enhancement. We also highlight the facile catalyst preparation approach that brought significant convenience to catalyst optimization. Based on our own studies, we then discuss directions of the catalyst research effort that are not immediately necessary or desirable, including (1) systematic study on the downstream aspects of under-developed catalysts, (2) random integration with hot concepts without a clear rationale, and (3) excessive and decorative experiments. We further address some general concerns regarding using H₂ and PGMs in the catalytic system. Finally, we recommend future catalyst development in both “fundamental” and “applied” aspects. The purpose of this perspective is to remove major misconceptions about reductive catalysis research and bring back significant innovations for both scientific advancements and engineering applications to benefit environmental protection.     

Co-pyrolysis of oil sludge with hydrogen-rich plastics in a vertical stirring reactor: Kinetic analysis,emissions, and products

● Collaborative treatment of plastics and OS was established to improve oil quality. ● PE addition successfully improved OS pyrolysis process by deploying H/C_eff ratio. ● Higher H/C_eff ratio promoted cracking to obtain more gas and light oil fractions. ● The degradation of PE and OS was promoted each other under their temperature range. Pyrolysis is an effective method to treat oily sludge (OS) due to its balance between oil recovery and nonhazardous disposal. However, tank bottom OS contains a high content of heavy fractions, which creates obstacles for pyrolysis due to the high activation energy. The incomplete cracking of macromolecules and secondary polymerization decreases the oil quality and causes coking during the operation process. This study introduced polyethylene (PE) into OS to deploy the H/C_eff ratio of feedstocks for pyrolysis. A strong interaction between OS and PE during copyrolysis could be observed from the TG/DTG curves. PE tightly participated in OS degradation, while OS also promoted PE degradation at high temperature. Apparent pits were generated in solid residues from copyrolysis, which was attributed to the uniform and violent gas release. In addition to HCN, other nitrogenous and sulphurous pollutants were inhibited. Accordingly, more gas products were attained after PE addition with more value-added compositions of alkanes and alkenes. Although the oil yield decreased after PE addition, the oil products from copyrolysis possessed higher heating values and higher contents of light fractions with short chains as well as paraffins. Consequently, copyrolysis of OS and PE significantly improved the pyrolysis process and resulted in high oil quality.     

Effect of ambient polycyclic aromatic hydrocarbons and nicotine on the structure of Aβ42 protein

● B[a]P, nicotine and phenanthrene molecules altered the secondary structure of Aβ₄₂. ● β-content of the peptide was significantly enhanced in the presence of the PAHs. ● Nicotine made stable cluster with Aβ₄₂ peptide via hydrogen bonds. ● Phenanthrene due to its small size, interfered with the Aβ₄₂ monomer more strongly. Recent studies have correlated the chronic impact of ambient environmental pollutants like polycyclic aromatic hydrocarbons (PAHs) with the progression of neurodegenerative disorders, either by using statistical data from various cities, or via tracking biomarkers during in-vivo experiments. Among different neurodegenerative disorders, PAHs are known to cause increased risk for Alzheimer’s disease, related to the development of amyloid beta (Aβ) peptide oligomers. However, the complex molecular interactions between peptide monomers and organic pollutants remains obscured. In this work, we performed an atomistic molecular dynamics study via GROMACS to investigate the structure of Aβ₄₂ peptide monomer in the presence of benzo[a]pyrene, nicotine, and phenanthrene. Interestingly the results revealed strong hydrophobic, and hydrogen-bond based interactions between Aβ peptides and these environmental pollutants that resulted in the formation of stable intermolecular clusters. The strong interactions affected the secondary structure of the Aβ₄₂ peptide in the presence of the organic pollutants, with almost 50 % decrease in the α-helix and 2 %–10 % increase in the β-sheets of the peptide. Overall, the undergoing changes in the secondary structure of the peptide monomer in the presence of the pollutants under the study indicates an enhanced formation of Aβ peptide oligomers, and consequent progression of Alzheimer’s disease.     

Collaborative control of fine particles and ozone required in China for health benefit

● Increased DAAO offsets 3/4 of the decrease of DAAP in 2013–2020. ● DAAO increases are mainly due to O₃ concentration increase and population aging. ● Health benefit from PM_2.5 reduction after 2017 is larger than that before 2017. ● Reducing PM_2.5 concentration by 1% results in 0.6% reduction of DAAP. ● Reducing O₃ concentration by 1% results in 2% reduction of DAAO. PM_2.5 concentration declined significantly nationwide, while O₃ concentration increased in most regions in China in 2013–2020. Recent evidences proved that peak season O₃ is related to increased death risk from non-accidental and respiratory diseases. Based on these new evidences, we estimate excess deaths associated with long-term exposure to ambient PM_2.5 and O₃ in China following the counterfactual analytic framework from Global Burden Disease. Excess deaths from non-accidental diseases associated with long-term exposure to ambient O₃ in China reaches to 579 (95% confidential interval (CI): 93, 990) thousand in 2020, which has been significantly underestimated in previous studies. In addition, the increased excess deaths associated with long-term O₃ exposure (234 (95% CI: 177, 282) thousand) in 2013–2020 offset three quarters of the avoided excess deaths (302 (95% CI: 244, 366) thousand) mainly due to PM_2.5 exposure reduction. In key regions (the North China Plain, the Yangtze River Delta and the Fen-Wei Plain), the former is even larger than the latter, particularly in 2017–2020. Health benefit of PM_2.5 concentration reduction offsets the adverse effects of population growth and aging on excess deaths attributed to PM_2.5 exposure. Increase of excess deaths associated with O₃ exposure is mainly due to the strong increase of O₃ concentration, followed by population aging. Considering the faster population aging process in the future, collaborative control, and faster reduction of PM_2.5 and O₃ are needed to reduce the associated excess deaths.     

Effect of Cu-ZSM-5 catalysts with different CuO particle size on selective catalytic oxidation of N,N-Dimethylformamide

● A series of Cu-ZSM-5 catalysts were tested for DMF selective catalytic oxidation. ● Cu-6 nm samples showed the best catalytic activity and N₂ selectivity. ● Redox properties and chemisorbed oxygen impact on DMF catalytic oxidation. ● Isolated Cu²⁺ species and weak acidity have effects on the generation of N₂. N, N-Dimethylformamide (DMF), a nitrogen-containing volatile organic compound (NVOC) with high emissions from the spray industry, has attracted increasing attention. In this study, Cu-ZSM-5 catalysts with different CuO particle sizes of 3, 6, 9 and 12 nm were synthesized and tested for DMF selective catalytic oxidation. The crystal structure and physicochemical properties of the catalyst were studied by various characterization methods. The catalytic activity increases with increasing CuO particle size, and complete conversion can be achieved at 300–350 °C. The Cu-12 nm catalyst has the highest catalytic activity and can achieve complete conversion at 300 °C. The Cu-6 nm sample has the highest N₂ selectivity at lower temperatures, reaching 95% at 300 °C. The activity of the catalysts is determined by the surface CuO cluster species, the bulk CuO species and the chemisorbed surface oxygen species. The high N₂ selectivity of the catalyst is attributed to the ratio of isolated Cu²⁺ and bulk CuO species, and weak acidity is beneficial to the formation of N₂. The results in this work will provide a new design of NVOC catalytic oxidation catalysts.     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

Development of machine learning multi-city model for municipal solid waste generation prediction

● A database of municipal solid waste (MSW) generation in China was established. ● An accurate MSW generation prediction model (WGMod) was constructed. ● Key factors affecting MSW generation were identified. ● MSW trends generation in Beijing and Shenzhen in the near future are projected. Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.     

Homogeneous and heterogeneous photolysis of nitrate in the atmosphere: state of the science,current research needs,and future prospects

● Recent advances in the photolysis of nitrate/HNO₃ are reviewed. ● Mechanisms and key factors affecting the photolysis of nitrate/HNO₃ are summarized. ● Atmospheric implications and future research recommendations are provided. Nitrate is an important component of atmospheric particulate matter and affects air quality, climate, human health, and the ecosystem. Nitrate was previously considered a permanent sink for nitrogen oxides (NO_x). However, this viewpoint has been challenged in recent years because growing research evidence has shown the transformation of nitrate into NO_x (i.e., renoxification). The photolysis of nitrate/HNO₃, especially in the particulate phase or adsorbed on particles, can be a significant renoxification process in the atmosphere. The formation and photolysis of nitrate in aerosol not only change the diurnal variation of NO_x, but also provide long-distance transport of NO_x in the form of nitrate, which affects local and regional atmospheric chemistry and air quality. This review summarizes recent advances in the fundamental understanding of the photolysis of nitrate/HNO₃ under various atmospheric conditions, with a focus on mechanisms and key factors affecting the process. The atmospheric implications are discussed and future research is recommended.     

Enrichment and transfer of polycyclic aromatic hydrocarbons (PAHs) through dust aerosol generation from soil to the air

● Compositional patterns of PAHs in dust aerosol vary from soil during dust generation. ● The EF of PAH in dust aerosol is affected by soil texture and soil PAH concentration. ● The sizes of dust aerosol play an important role in the enrichment of HMW-PAHs. Polycyclic aromatic hydrocarbons (PAHs) are major organic pollutants in soil. It is known that they are released to the atmosphere by wind via dust aerosol generation. However, it remains unclear how these pollutants are transferred through the air/soil interface. In this study, dust aerosols were generated in the laboratory using soils (sandy loam and loam) with various physicochemical properties. The PAH concentrations of these soils and their generated dust aerosol were measured, showing that the enrichment factors (EFs) of PAHs were affected by soil texture, PAH contamination level, molecular weight of PAH species and aerosol sizes. The PAHs with higher EFs (6.24–123.35 in dust PM_2.5; 7.02–47.65 in dust PM₁₀) usually had high molecular weights with more than four aromatic rings. In addition, the positive correlation between EFs of PAHs and the total OC_aerosol content of dust aerosol in different particle sizes was also statistically significant (r = 0.440, P < 0.05). This work provides insights into the relationship between atmospheric PAHs and the contaminated soils and the transfer process of PAHs through the soil-air interface.     

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

● A machine learning model was used to identify lake nutrient pollution sources. ● XGBoost model showed the best performance for lake water quality prediction. ● Model feature size was reduced by screening the key features with the MIC method. ● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources. ● Next-month lake TN and TP concentrations were predicted accurately. Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R² of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.     

PM2.5 concentration declining saves health expenditure in China

● Monthly hospitalization expenses are sensitive to increases in PM_2.5 exposure. ● The increased PM_2.5 causes patients with CHD and LRI to stay longer in the hospital. ● The impact of PM_2.5 on total expenses for stroke is greater in southern China. ● Males may be more sensitive to air pollution than females. Air pollution has been a severe issue in China. Exposure to PM_2.5 has adverse health effects and causes economic losses. This study investigated the economic impact of exposure to PM_2.5 pollution using monthly city-level data covering 88.5 million urban employees in 2016 and 2017. This study mainly focused on three expenditure indicators to measure the economic impact considering lower respiratory infections (LRIs), coronary heart disease (CHD), and stroke. The results show that a 10 µg/m³ increase in PM_2.5 would cause total monthly expenses of LRIs, CHD, and stroke to increase by 0.226%, 0.237%, and 0.374%, respectively. We also found that LRI, CHD, and stroke hospital admissions increased significantly by 10%, 8.42%, and 5.64%, respectively. Furthermore, the total hospital stays of LRIs, CHDs, and strokes increased by 2.49%, 2. 51%, and 1.64%, respectively. Our findings also suggest heterogeneous impacts of PM_2.5 exposures by sex and across regions, but no statistical evidence shows significant differences between the older and younger adult subgroups. Our results provide several policy implications for reducing unequal public health expenditures in overpolluted countries.     

Technologies for pollutant removal and resource recovery from blackwater: a review

● Blackwater is the main source of organics and nutrients in domestic wastewater. ● Various treatment methods can be applied for resource recovery from blackwater. ● Blackwater treatment systems of high integration and efficiency are the future trend. ● More research is needed for the practical use of blackwater treatment systems. Blackwater (BW), consisting of feces, urine, flushing water and toilet paper, makes up an important portion of domestic wastewater. The improper disposal of BW may lead to environmental pollution and disease transmission, threatening the sustainable development of the world. Rich in nutrients and organic matter, BW could be treated for resource recovery and reuse through various approaches. Aimed at providing guidance for the future development of BW treatment and resource recovery, this paper presented a literature review of BWs produced in different countries and types of toilets, including their physiochemical characteristics, and current treatment and resource recovery strategies. The degradation and utilization of carbon (C), nitrogen (N) and phosphorus (P) within BW are underlined. The performance of different systems was classified and summarized. Among all the treating systems, biological and ecological systems have been long and widely applied for BW treatment, showing their universality and operability in nutrients and energy recovery, but they are either slow or ineffective in removal of some refractory pollutants. Novel processes, especially advanced oxidation processes (AOPs), are becoming increasingly extensively studied in BW treatment because of their high efficiency, especially for the removal of micropollutants and pathogens. This review could serve as an instructive guidance for the design and optimization of BW treatment technologies, aiming to help in the fulfilment of sustainable human excreta management.